机器人 / 具身智能 - arXivDaily 专题

2002.10621 2020-02-26 cs.LG cs.RO cs.SY eess.SP eess.SY stat.ML 62%

Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements

Alberto Dalla Libera, Diego Romeres, Devesh K. Jha, Bill Yerazunis, Daniel Nikovski

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments Accepted at RA-L

2002.09884 2020-02-25 cs.LG cs.AI stat.ML 62%

Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations

Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee, Nan Ye

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments Accepted to ICLR 2020

2002.08242 2020-02-20 cs.CV cs.AI 62%

AI Online Filters to Real World Image Recognition

Hai Xiao, Jin Shang, Mengyuan Huang

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.CV

2002.07956 2020-02-20 cs.LG cs.AI stat.ML 62%

Curriculum in Gradient-Based Meta-Reinforcement Learning

Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy, Chris J. Pal, Liam Paull

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments 11 pages, 10 figures

1906.07516 2020-02-12 cs.LG cs.AI stat.ML 62%

Robust Reinforcement Learning for Continuous Control with Model Misspecification

Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.AI、cs.LG

1810.01032 2020-02-04 cs.LG cs.CR cs.CV stat.ML 62%

Reinforcement Learning with Perturbed Rewards

Jingkang Wang, Yang Liu, Bo Li

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.CV、cs.LG

Comments AAAI 2020 (Spotlight)

2001.03792 2020-01-14 cs.AI cs.RO 62%

Reward Engineering for Object Pick and Place Training

Raghav Nagpal, Achyuthan Unni Krishnan, Hanshen Yu

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.AI

1907.12392 2020-01-09 cs.LG cs.AI stat.ML 62%

A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment

Felix Leibfried, Sergio Pascual-Diaz, Jordi Grau-Moya

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

Comments Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019

1910.12639 2020-01-01 eess.SY cs.AI cs.MA cs.RO cs.SY 62%

MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding

Wenbo Zhang, Osbert Bastani, Vijay Kumar

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.AI

1912.11531 2019-12-30 cs.CR cs.AI cs.LG 62%

Pseudo Random Number Generation: a Reinforcement Learning approach

Luca Pasqualini, Maurizio Parton

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Comments 13 pages, 8 figures

1910.01465 2019-12-03 cs.LG cs.AI cs.MA stat.ML 62%

Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

Johannes Ackermann, Volker Gabler, Takayuki Osa, Masashi Sugiyama

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.AI、cs.LG

Comments Accepted for the Deep RL Workshop at NeurIPS 2019; Changes for v2: Changed Figures 3,4, due to an error in the implementation of MATD3. Please refer to this version for fair evaluation

1911.10641 2019-12-03 cs.LG cs.AI math.OC 62%

ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems

Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, Chun Ye

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

1903.00401 2019-11-25 cs.AI cs.CL cs.CV 62%

Learning To Follow Directions in Street View

Karl Moritz Hermann, Mateusz Malinowski, Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Raia Hadsell

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.CV

Journal ref AAAI 2020

1910.10754 2019-10-25 cs.LG cs.RO stat.ML 62%

Learning Q-network for Active Information Acquisition

Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments IROS 2019, Video https://youtu.be/0ZFyOWJ2ulo

Journal ref IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019

1910.09667 2019-10-23 cs.RO cs.LG cs.SY eess.SY 62%

Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning

Guillaume Bellegarda, Katie Byl

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

1910.08811 2019-10-22 cs.CV cs.RO 62%

Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning

Juil Sock, Guillermo Garcia-Hernando, Tae-Kyun Kim

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.CV

1909.12989 2019-10-14 cs.LG cs.RO stat.ME 62%

SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning

Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu, Orien Zeng, Anchit Gupta, Joan Creus-Costa, Silvio Savarese, Li Fei-Fei

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.LG

Comments Technical report of the SURREAL system. See more details at https://surreal.stanford.edu

1907.05634 2019-10-14 cs.LG cs.AI stat.ML 62%

Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

Yuping Luo, Huazhe Xu, Tengyu Ma

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

1909.13599 2019-10-08 cs.RO cs.AI 62%

End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning

Efe Camci, Erdal Kayacan

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.RO、cs.AI

Comments IROS 2019 Workshop, Learning Representations for Planning and Control

1909.12925 2019-10-01 cs.AI cs.LG 62%

Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals

Anahita Mohseni-Kabir, David Isele, Kikuo Fujimura

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.AI、cs.LG

Journal ref ICRA 2019

1909.01500 2019-09-25 cs.LG cs.AI 62%

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

Adam Stooke, Pieter Abbeel

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.AI、cs.LG

Comments v2: Updated learning curves for SAC and TD3, improved by bootstrapping value-function when trajectory ends due to time limit, and switching to newer SAC version, now referenced

1909.08052 2019-09-19 cs.HC cs.AI cs.RO 62%

Towards an Adaptive Robot for Sports and Rehabilitation Coaching

Martin K. Ross, Frank Broz, Lynne Baillie

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.AI

Comments AI-HRI 2019

1909.07374 2019-09-18 cs.LG cs.RO stat.ML 62%

A Linearly Constrained Nonparametric Framework for Imitation Learning

Yanlong Huang, Darwin G. Caldwell

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments 7 pages

1906.09090 2019-09-04 cs.LG cs.RO 62%

Entropic Risk Measure in Policy Search

David Nass, Boris Belousov, Jan Peters

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

1908.06973 2019-08-21 cs.LG cs.AI 62%

Reinforcement Learning Applications

Yuxi Li

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

1908.06012 2019-08-19 cs.LG cs.AI stat.ML 62%

Model-based Lookahead Reinforcement Learning

Zhang-Wei Hong, Joni Pajarinen, Jan Peters

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

1908.05546 2019-08-16 cs.RO cs.LG 62%

Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction

Mohammad Thabet, Massimiliano Patacchiola, Angelo Cangelosi

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG

Comments Accepted for IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)

1810.06979 2019-08-13 cs.RO cs.HC cs.LG 62%

Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning

Yuan Gao, Fangkai Yang, Martin Frisk, Daniel Hernandez, Christopher Peters, Ginevra Castellano

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.RO、cs.LG

Comments accepted for The 28th IEEE International Conference on Robot & Human Interactive Communication (Ro-Man)

1811.11615 2019-07-26 cs.RO cs.LG 62%

Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge

Gabriel Hartmann, Zvi Shiller, Amos Azaria

专题命中模仿学习与强化学习：navigation(abstract)；分类 cs.RO、cs.LG

1807.08364 2019-07-23 cs.LG cs.AI 62%

EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning

Kunal Menda, Katherine Driggs-Campbell, Mykel J. Kochenderfer

专题命中模仿学习与强化学习：robotics(abstract)；分类 cs.AI、cs.LG

Comments Accepted to the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)

1. 模仿学习与强化学习 4134 篇

Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements

Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations

AI Online Filters to Real World Image Recognition

Curriculum in Gradient-Based Meta-Reinforcement Learning

Robust Reinforcement Learning for Continuous Control with Model Misspecification

Reinforcement Learning with Perturbed Rewards

Reward Engineering for Object Pick and Place Training

A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment

MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding

Pseudo Random Number Generation: a Reinforcement Learning approach

Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems

Learning To Follow Directions in Street View

Learning Q-network for Active Information Acquisition

Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning

Active 6D Multi-Object Pose Estimation in Cluttered Scenarios with Deep Reinforcement Learning

SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning

Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning

Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

Towards an Adaptive Robot for Sports and Rehabilitation Coaching

A Linearly Constrained Nonparametric Framework for Imitation Learning

Entropic Risk Measure in Policy Search

Reinforcement Learning Applications

Model-based Lookahead Reinforcement Learning

Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction

Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning

Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge

EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning